Modeling Attacks on Photo-ID Documents and Applying Media Forensics for the Detection of Facial Morphing

Since 2014, a novel approach to attack face image based person verification designated as face morphing attack has been actively discussed in the biometric and media forensics communities. Up until that point, modern travel documents were considered to be extremely hard to forge or to successfully manipulate. In the case of template-targeting attacks like facial morphing, the face verification process becomes vulnerable, making it a necessity to design protection mechanisms. In this paper, a new modeling approach for face morphing attacks is introduced. We start with a life-cycle model for photo-ID documents. We extend this model by an image editing history model, allowing for a precise description of attack realizations as a foundation for performing media forensics as well as training and testing scenarios for the attack detectors. On the basis of these modeling approaches, two different realizations of the face morphing attack as well as a forensic morphing detector are implemented and evaluated. The design of the feature space for the detector is based on the idea that the blending operation in the morphing pipeline causes the reduction of face details. To quantify this reduction, we adopt features implemented in the OpenCV image processing library, namely the number of SIFT, SURF, ORB, FAST and AGAST keypoints in the face region as well as the loss of edge-information with Canny and Sobel edge operators. Our morphing detector is trained with 2000 self-acquired authentic and 2000 morphed images captured with three camera types (Canon EOS 1200D, Nikon D 3300, Nikon Coolpix A100) and tested with authentic and morphed face images from a public database. Morphing detection accuracies of a decision tree classifier vary from 81.3% to 98% for different training and test scenarios.